Michael

Michael is currently a Data Science Fellow at the NYC Data Science Academy. He graduated from the Wharton School of the University of Pennsylvania with a B.S. in Economics before securing the position of Investment Banking Analyst at UBS. As his passion for artificial intelligence grew, Michael decided that he wanted to to shift his career in the direction of expanding the application of machine learning models and algorithms.
Given his background in economic, financial, public policy analysis from his undergraduate and professional career, his goal was to apply AI to for quantum computation, military technology, and economic research. He was able to incorporate those interests in a number of data science projects. They include the following: a web scraper that collects information off of Twitter in order to identify practices that raise social media awareness of the quantum computing industry; an interactive R Shiny app that visualizes the U.S. commodity trade market and identifies policies that the U.S. can improve for the health of its long-term economy; and a real estate analysis model that employs machine learning techniques to predict home prices.
Michael’s interdisciplinary reach and enthusiasm for teamwork are key drivers for his successful project completion. He plans to continuously sharpen his skills in the exciting field of data science. In his spare time, he likes to train Brazilian jiu-jitsu.